Abstract-In an EOQ model all items are treated individually and their dependence on each other is not considered. But practically, the sale of one item could affect the sale of other items too. Thus, when the cross selling effects are considered, the frequent itemsets should be treated as an individual item and their economic order quantity (EOQ) should be estimated accordingly. Moreover, cross selling effects becomes more prominent when items are defective in nature. In this paper, we have estimated EOQ of imperfect quality items while considering cross selling effects. First, we have applied data mining techniques to find the relation between itemsets. Second, we applied the calculated cross selling effect to estimate the EOQ. Results have been validated with the help of numerical example.

Due to the increased availability of individual customer data, it is possible to predict customer buying pattern. Customers can be segmented using clustering algorithms based on various parameters such as Frequency, Recency and Monetary values (RFM). The data can further be analyzed to infer rules among two or more purchases of the customer. In this chapter we will present a clustering algorithm, enhanced k- means algorithm, which is based on k- means algorithm to divide customers into various segments. After segmentation, each segment is mined with the help of a priori algorithm to infer rules so that the customer's purchase behavior can be predicted. From large number of association rules with sufficient coverage, the customer's purchasing pattern can be predicted. Experiment on real database is implemented to evaluate the performance on effectiveness and utility of the approach. The results show that the proposed approach can gain a well insight into customers' segmentation and thus their behavior can be predicted.

Clustering is a process of grouping a set of similar data objects within the same group based on similarity criteria (i.e. based on a set of attributes). There are many clustering algorithms. The objective of this paper is to perform a comparative analysis of four clustering algorithms namely Kmeans algorithm, Hierarchical algorithm, Expectation and maximization algorithm and Density based algorithm. These algorithms are compared in terms of efficiency and accuracy, using WEKA tool. The data for clustering is used in normalized and as well as unnormalized format. In terms of efficiency and accuracy K-means produces better results as compared to other algorithms.

Introduction: Optimal inventory levels are necessary for a firm to avoid shortage/excess of an item. Shortage of an item leads to stock out conditions which results in loss of profit. When items are correlated with each other, stock out condition of one item may result in non purchase of its associated items also which, in turn, further brings down the profit. In this paper, this loss in profit is used to modify opportunity cost of an item resulting in its modified EOQ. Method: One illustrative example has been discussed which incorporates purchase dependencies in retail multi-item inventory management. The model discussed in this research paper will be motivational for researchers and inventory managers and provides a method for incorporating correlation among items while managing inventory. Result: The EOQs of items are estimated both by using traditional method and then by using modified opportunity cost (modeled as loss profit). Results show that in frequent item set A, B, D, EOQs of all three items increased when correlation among them is considered resulting in increase in profit. Conclusion: One of the major focus areas of inventory management is to determine when and how much quantity of items needs to be ordered so that total inventory cost can be minimized and profit of a firm can be maximized. However, while calculating the true value of an item and the profit it brings to the firm, it is very essential to analyze its effect on the sale of other items. Asso-ciation rule mining provides a way to correlate items by calculating support and confidence factor. Discussion: In inventory management system, for increasing the profit of a firm, EOQs of items need to be calculated in order to avoid shortage or excess of inventory. For explaining the approach a very small database is taken consisting of only 5 items and 10 transactions therefore the increase in profit is minimal however, when this approach applies on real database consisting of thousands of items and transactions, the increase in profit will be significant.

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